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import numpy as np
from .SODA.soda import SODA
from .SODA.dataset import ANETCaptions
def eval_tool(prediction, referneces=None, metric='Meteor', soda_type='c', verbose=False, print_matrix=False):
args = type('args', (object,), {})()
args.prediction = prediction
args.references = referneces
args.metric = metric
args.soda_type = soda_type
args.tious = [0.3, 0.5, 0.7, 0.9]
args.verbose = verbose
args.multi_reference = False
data = ANETCaptions.from_load_files(args.references,
args.prediction,
multi_reference=args.multi_reference,
verbose=args.verbose,
)
data.preprocess()
if args.soda_type == 'a':
tious = args.tious
else:
tious = None
evaluator = SODA(data,
soda_type=args.soda_type,
tious=tious,
scorer=args.metric,
verbose=args.verbose,
print_matrix=print_matrix
)
result = evaluator.evaluate()
return result
def eval_soda(p, ref_list,verbose=False, print_matrix=False):
score_sum = []
for ref in ref_list:
r = eval_tool(prediction=p, referneces=[ref], verbose=verbose, soda_type='c', print_matrix=print_matrix)
score_sum.append(r['Meteor'])
soda_avg = np.mean(score_sum, axis=0) #[avg_pre, avg_rec, avg_f1]
soda_c_avg = soda_avg[-1]
results = {'soda_c': soda_c_avg}
return results |